Hierarchical Optimal Scheduling of Photovoltaic Community Considering Electric Vehicle (EV) Battery Life

Aiming at the problem that a large number of electric vehicles (EV) are charging disorderly and the uncertainty of photovoltaic brings adverse effects such as load fluctuation to the grid, based on the day-ahead prediction data of intelligent photovoltaic community, this paper proposed a multi-objective optimal scheduling strategy considering the battery life of electric vehicles. In order to work out a practical and feasible scheduling plan, a layered optimization scheduling strategy was established. The first layer, aimed at minimizing total load fluctuation in the photovoltaic community, was based on day-ahead forecast data, and took the 24 hours before the day as the scheduling scale. The second layer established a real-time scheduling scheme according to the real-time actual data, and tracked the day-ahead optimization scheduling target of the first layer through actual charging and discharging arrangement of each electric vehicle. At the same time, in order to ensure the interests of electric vehicle users, the objective function of minimum charge-discharge switching times was added in the second layer optimization to reduce the wear of frequent charge-discharge switching on battery life. Finally, the simulation results show that the scheduling strategy can reduce the load fluctuation of the power grid while reducing the adverse impact on the battery life of electric vehicles.